TXSelect:一种多任务学习模型,用于识别分泌效应因子
Jing Li1,2,3, Qing Liu4, Quan Zou2
1Department of Microbiology, University of Hong Kong, Hong Kong, China.
PLoS computational biology
|November 6, 2025
概括
我们开发了TXSelect,这是一个计算工具来分类细菌分泌效应器 (TXSE). 该框架准确地识别了多种效应因子类型,有助于理解病原体机制和开发新疗法.
科学领域:
- 微生物学 微生物学
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 致病微生物利用分泌效应器来操纵宿主过程,影响生存和致病性.
- 由于序列和结构异质性,对各种细菌效应剂 (I,II,III,IV和VI型分泌效应剂 - - TXSE) 进行准确的分类是具有挑战性的.
研究的目的:
- 开发一个高效的计算框架,TXSelect,用于同时分类多个TXSE类型.
- 整合先进的蛋白质特征,以提高分类准确性和生物洞察力.
主要方法:
- 开发了TXSelect,这是一个多任务学习框架,具有共享的骨干和TXSE分类的任务特定头部.
- 综合蛋白质嵌入特征来自进化规模建模 (ESM) N-终端平均值与经典描述符:基于距离的残留物 (DR) 和分裂氨基酸组成一般 (SC-PseAAC-General).
- 通过严格的验证和测试评估特征组合和模型性能,包括统一的多重近似和投影用于可视化.
主要成果:
- 最佳特征组合 (ESM N-终端平均值 + DR + SC-PseAAC) 实现了高精度,验证F1得分为0.867和测试F1得分为0.8645.
- 在不同的TXSE类型中,TXSelect展示了强大的泛化能力.
- 模型的可解释性和区分能力通过全面的评估和可视化技术来验证.
结论:
- TXSelect提供了一个准确和高效的计算工具来分类细菌TXSE.
- 该框架支持对病原体与宿主相互作用的更深入的生物学理解.
- 这种工具在识别传染病治疗点方面具有潜在的应用.
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